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Update README to reflect installation notes and general things
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README.md
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README.md
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FabFire Adapter
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===
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The FabFire adapter translates mqtt messages from the reader hardware to api calls to bffhd.
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# Usage
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## Configuration in `config.toml`
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The FabFire adapter translates MQTT messages from the FabReader hardware to API calls to bffhd.
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# Installation
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## With python3-venv
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Easy, native installation without overhead is possible with python3 virtual environment:
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```shell
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cd /opt/fabinfra/adapters/
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git clone https://gitlab.com/fabinfra/fabaccess/fabfire_adapter.git --recursive
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cd fabfire_adapter/
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python3 -m venv env
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. env/bin/activate
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pip3 install -r requirements.txt
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```
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## With the Dockerfile and Podman
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You can also install the fabfire adapter using Docker or Podman:
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```shell
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podman build -f Dockerfile -t fabinfra/fabfire_adapter
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podman run localhost/fabinfra/fabfire_adapter:latest
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#cleanup if required:
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podman system prune
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```
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# Configuration
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Edit the config file example provided in `config/config.toml`
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1. Set `hostname` and `port` for your MQTT Broker and bffhd instance
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2. Add your readers to the `[readers]` section. Every reader needs a unique subsection name:
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```toml
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[readers]
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2. Add your desired readers to the `[readers]` section. Every reader needs a unique subsection name:
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```toml
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[readers]
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[readers.REPLACEME]
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id = "111"
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machine = "urn:fabaccess:resource:Testmachine"
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@ -15,14 +47,16 @@ The FabFire adapter translates mqtt messages from the reader hardware to api cal
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[readers.REPLACEMETOO]
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id = "222"
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machine = "urn:fabaccess:resource:Another"
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```
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```
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# Running
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1. Run the adapter:
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2. ```shell
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python3 main.py
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```
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## Running
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1. Install the requirements with `pip install -r requirements.txt`
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2. Run the adapter with at least python 3.7
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```shell
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python3 main.py
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```
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3. The adapter has to remain running for the readers to work
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# Provisioning
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